Facebook’s deployed neural networks process more than 6 million predictions per second;

25% of Facebook engineers are now using AI and machine learning APIs and infrastructure;

Facebook has deployed more than 40 PFLOP/s of GPU capability in house to support deep learning across their organization.

As neural networks get deeper and more complex, they provide a dramatic increase in accuracy, but training these higher accuracy networks requires much higher computation time, and their complexity increases prediction latency.

To satisfy this insatiable need for performance, NVIDIA created the DGX-1. The system can be deployed quickly for plug-and-play use by deep learning researchers and data scientists.

The architecture of DGX-1 draws on NVIDIA’s experience in the field of high-performance computing as well as knowledge gained from optimizing deep learning frameworks on NVIDIA GPUs with every major cloud service provider and multiple Fortune 1000 companies.

Best throughput achievable on each platform. DGX-1 (P100) using FP32, DGX-1 (V100) using mixed precision (FP16 and FP32) using deep learning framework containers version 17.11

More productivity and performance benefits come from the fact that DGX-1 is an integrated system to enable data scientists and A.I. researchers to deploy deep learning frameworks and applications on DGX-1 with minimal setup effort.

About Brad Nemire

Brad Nemire is on the Developer Marketing team and loves reading about all of the fascinating research being done by developers using NVIDIA GPUs. Reach out to Brad on Twitter @BradNemire and let him know how you’re using GPUs to accelerate your research. Brad graduated from San Diego State University and currently resides in San Jose, CA. Follow @BradNemire on Twitter